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Autonomous multirobot excavation for lunar applications

Published online by Cambridge University Press:  25 January 2017

Jekanthan Thangavelautham ,
Kenneth Law ,
Terence Fu ,
Nader Abu El Samid ,
Alexander D. S. Smith  and
Gabriele M. T. D'Eleuterio
Jekanthan Thangavelautham*
Affiliation:
School of Earth and Space Exploration, Arizona State University, 781 E Terrace Mall, Tempe, AZ 85287, USA
Kenneth Law
Affiliation:
David Schaeffer and Associates, Markham, ON, Canada. E-mail: [email protected]
Terence Fu
Affiliation:
University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada. E-mails: [email protected], [email protected], [email protected]
Nader Abu El Samid
Affiliation:
MDA Space Missions, 9445 Airport Road, Brampton, ON L6S 4J3, Canada. E-mail: [email protected]
Alexander D. S. Smith
Affiliation:
University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada. E-mails: [email protected], [email protected], [email protected]
Gabriele M. T. D'Eleuterio
Affiliation:
University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada. E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

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In this paper, a control approach called Artificial Neural Tissue (ANT) is applied to multirobot excavation for lunar base preparation tasks including clearing landing pads and burying of habitat modules. We show for the first time, a team of autonomous robots excavating a terrain to match a given three-dimensional (3D) blueprint. Constructing mounds around landing pads will provide physical shielding from debris during launch/landing. Burying a human habitat modules under 0.5 m of lunar regolith is expected to provide both radiation shielding and maintain temperatures of −25 °C. This minimizes base life-support complexity and reduces launch mass. ANT is compelling for a lunar mission because it does not require a team of astronauts for excavation and it requires minimal supervision. The robot teams are shown to autonomously interpret blueprints, excavate and prepare sites for a lunar base. Because little pre-programmed knowledge is provided, the controllers discover creative techniques. ANT evolves techniques such as slot-dozing that would otherwise require excavation experts. This is critical in making an excavation mission feasible when it is prohibitively expensive to send astronauts. The controllers evolve elaborate negotiation behaviors to work in close quarters. These and other techniques such as concurrent evolution of the controller and team size are shown to tackle problem of antagonism, when too many robots interfere reducing the overall efficiency or worse, resulting in gridlock. Although many challenges remain with this technology, our work shows a compelling pathway for field testing this approach.

Keywords

neural-networksexcavationmultirobotshuman-competitiveISRU
Type
Articles
Information
Robotica , Volume 35 , Issue 12 , December 2017 , pp. 2330 - 2362
Copyright
Copyright © Cambridge University Press 2017 

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